The process of innovation within organizations remains largely untouched by the general trend toward improved efficiency through automation. The traditional model of stimulating innovative thought is through the application of psychological techniques, such as brainstorming. These techniques bring limited improvement to the process.
More recently, there have emerged a number of computer-based technologies that can be applied by a researcher or designer that is considering the creation or improvement of a device, process, or other system. These technologies can be referred to as “problem analysis tools.”
Problem analysis tools assist the user by enabling the user to consider a complex system and identify discrete problems which should be addressed. These tools accomplish this by providing computer-based interfaces which assist in the application of well understood methods of problem analysis including, but are not limited to, root cause analysis, TRIZ (a Russian acronym for “Teoriya Resheniya Izobretatelskikh Zadatch”), value engineering, system functional analysis, and system benchmarking. TRIZ is a methodology, tool set, knowledge base, and model-based technology for generating innovative ideas and solutions for problem solving. An example of such a tool, called TechOptimizer™, is a computer system marketed by Invention Machine Corporation of Boston, Mass. The technology used in TechOptimizer™ to assist in problem analysis is partially described in U.S. Pat. No. 6,056,428 and U.S. Pat. No. 6,202,043. The system disclosed in these two patents is fully described in TechOptimizer™ user guide, version 4.0, Invention Machine Corporation, Boston, Mass.
The TechOptimizer™ software suite includes a module, which allows a user to build a system functional model of a design and/or technological process, to perform value diagnostics of the design and/or technological process, identify a better (for example, higher value) configuration of the design and/or technological process, and identify what problem has to be solved in order to implement this new configuration.
A key deficiency with problem analysis tools is that while they greatly aid in the identification of specific issues to be addressed, the user of such tools is required to possess the knowledge about the problem being considered to adequately describe the problem or system in which the problem exists. In U.S. patent application Ser. No. 11/273,137, “System and Method for Problem Analysis,” filed Nov. 14, 2005, there is disclosed a method for providing user specific relevant information to assist in the modeling of problems, wherein the principle aspect of analysis is cause-effect relationships, and wherein a cause or effect statement is automatically reformulated as a natural language query. The query is submitted to a database, and the results of the query are returned, thereby greatly facilitating the process of identifying related cause-effect data.
However, there exists a class of situations in the use of problem analysis tools which make the use of these tools difficult and do not yield to the technique above. In these situations a researcher or designer must consider the nature of the system being examined. Whether the system being examined comprises a device, a process, an organization, or any other type of natural or artificial system, the researcher or designer must understand the make-up of the system, its constituent components, and the interactions among those components. This understanding helps lead the researcher to a deeper understanding of the problem and subsequently to solution concepts.
This can be easily understood by considering the process of the method of system analysis commonly referred to as “system functional modeling.” In this analytical process, the researcher can begin with a statement of a system under investigation, for example “milking stool.” The researcher will then consider what the components of the milking stool system are: stool legs, seat, handle, farmer, cow, floor, milk pail. The researcher will then consider for each pair of components of the system, what the functional interactions between the components are. For example, it may be identified that an interaction between the seat and the farmer is that the seat supports the farmer. The researcher will continue with iterative analysis until all known interactions have been identified and classified. Well understood principles of value engineering analysis may then be used to gain insights into the relative value contribution of each component to the overall system. This type of analysis is very effective and in wide use. However, it is also difficult because the researcher has no well defined methods for identifying the components of the system and their functional interactions. As a result, the method is largely dependant on personal domain knowledge.
A similar problem can be seen in the analytic method commonly referred to as “systems thinking.” In this method, practitioners identify parts of process system and explore the dynamic nature of the system, identifying interactions between elements, and looking for causal cycles. Just as with the case of the researcher performing a system functional modeling analysis, the systems thinking researcher is left to rely on personal domain knowledge to identify the components and interactions of any particular system.
In both of these situations, the user must construct a system model, and use his or her domain knowledge to identify components of the system and the interactions between those components. If the user does not have adequate domain knowledge, the user must conduct independent research using whatever means are available to find useful information. These means could include using books, public Internet search engines, private data subscription services, internal enterprise portals, or other sources of relevant technical information.